Adopt a Bacterium – an active and collaborative learning experience in microbiology based on social media
Bibliographic record
Abstract
The "Adopt a Bacterium" project is based on the use of social network as a tool in Microbiology undergraduate education, improving student learning and encouraging students to participate in collaborative learning. The approach involves active participation of both students and teachers, emphasizing knowledge exchange, based on widely used social media. Students were organized in groups and asked to adopt a specific bacterial genus and, subsequently, submit posts about "adopted genus". The formative assessment is based on posting information on Facebook®, and the summative assessment involves presentation of seminars about the adopted theme. To evaluate the project, students filled out three anonymous and voluntary surveys. Most of the students enjoyed the activities and positively evaluated the experience. A large amount of students declared a change in their attitude towards the way they processed information, especially regarding the use of scientific sources. Finally, we evaluated knowledge retention six months after the end of the course and students were able to recall relevant Microbiology concepts. Our results suggest that the "Adopt a Bacterium" project represents a useful strategy in Microbiology learning and may be applied to other academic fields.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".